import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     				=		logging.get_logger('transformers.models.encodec')

_SCREAMING_SNAKE_CASE     				=		{
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
_SCREAMING_SNAKE_CASE     				=		{
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
_SCREAMING_SNAKE_CASE     				=		{
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
_SCREAMING_SNAKE_CASE     				=		{
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
_SCREAMING_SNAKE_CASE     				=		{
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
_SCREAMING_SNAKE_CASE     				=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_SCREAMING_SNAKE_CASE     				=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_SCREAMING_SNAKE_CASE     				=		[]
_SCREAMING_SNAKE_CASE     				=		[]






def 					snake_case    (			snake_case__     :List[Any]  ,      snake_case__     :Optional[Any]  ,      snake_case__     :List[str]  ,      snake_case__     :Optional[Any]  ,      snake_case__     :Optional[Any])		->  Optional[int]:
       for attribute in key.split("""."""):
              _A	  =    getattr(snake_case__  ,      snake_case__)

       if weight_type is not None:
              _A	  =    getattr(snake_case__  ,      snake_case__).shape
       else:
              _A	  =    hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
                  F''' {value.shape} for {full_name}''')

       if weight_type == "weight":
              _A	  =    value
       elif weight_type == "weight_g":
              _A	  =    value
       elif weight_type == "weight_v":
              _A	  =    value
       elif weight_type == "bias":
              _A	  =    value
       elif weight_type == "running_mean":
              _A	  =    value
       elif weight_type == "running_var":
              _A	  =    value
       elif weight_type == "num_batches_tracked":
              _A	  =    value
       elif weight_type == "weight_ih_l0":
              _A	  =    value
       elif weight_type == "weight_hh_l0":
              _A	  =    value
       elif weight_type == "bias_ih_l0":
              _A	  =    value
       elif weight_type == "bias_hh_l0":
              _A	  =    value
       elif weight_type == "weight_ih_l1":
              _A	  =    value
       elif weight_type == "weight_hh_l1":
              _A	  =    value
       elif weight_type == "bias_ih_l1":
              _A	  =    value
       elif weight_type == "bias_hh_l1":
              _A	  =    value
       else:
              _A	  =    value

       logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''')






def 					snake_case    (			snake_case__     :Optional[Any]  ,      snake_case__     :Union[str, Any])		->  List[str]:
       for key in ignore_keys:
              if key.endswith(""".*"""):
                     if name.startswith(key[:-1]):
                            return True
              elif ".*." in key:
                     _A  ,    _A	  =    key.split(""".*.""")
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False






def 					snake_case    (			snake_case__     :Tuple  ,      snake_case__     :Union[str, Any]  ,      snake_case__     :Union[str, Any])		->  Optional[Any]:
       _A	  =    []

       if model_name == "encodec_24khz" or "encodec_32khz":
              _A	  =    MAPPING_24K
       elif model_name == "encodec_48khz":
              _A	  =    MAPPING_48K
       else:
              raise ValueError(F'''Unsupported model: {model_name}''')

       for name, value in orig_dict.items():
              if should_ignore(snake_case__  ,      snake_case__):
                     logger.info(F'''{name} was ignored''')
                     continue

              _A	  =    False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            _A  ,    _A	  =    key.split(""".*.""")
                            if prefix in name and suffix in name:
                                   _A	  =    suffix

                     if key in name:
                            # HACK otherwise .embed gets initialized with .embed_avg too
                            if key.endswith("""embed""") and name.endswith("""embed_avg"""):
                                   continue

                            _A	  =    True
                            if "*" in mapped_key:
                                   _A	  =    name.split(snake_case__)[0].split(""".""")[-2]
                                   _A	  =    mapped_key.replace("""*"""  ,      snake_case__)
                            if "weight_g" in name:
                                   _A	  =    """weight_g"""
                            elif "weight_v" in name:
                                   _A	  =    """weight_v"""
                            elif "weight_ih_l0" in name:
                                   _A	  =    """weight_ih_l0"""
                            elif "weight_hh_l0" in name:
                                   _A	  =    """weight_hh_l0"""
                            elif "bias_ih_l0" in name:
                                   _A	  =    """bias_ih_l0"""
                            elif "bias_hh_l0" in name:
                                   _A	  =    """bias_hh_l0"""
                            elif "weight_ih_l1" in name:
                                   _A	  =    """weight_ih_l1"""
                            elif "weight_hh_l1" in name:
                                   _A	  =    """weight_hh_l1"""
                            elif "bias_ih_l1" in name:
                                   _A	  =    """bias_ih_l1"""
                            elif "bias_hh_l1" in name:
                                   _A	  =    """bias_hh_l1"""
                            elif "bias" in name:
                                   _A	  =    """bias"""
                            elif "weight" in name:
                                   _A	  =    """weight"""
                            elif "running_mean" in name:
                                   _A	  =    """running_mean"""
                            elif "running_var" in name:
                                   _A	  =    """running_var"""
                            elif "num_batches_tracked" in name:
                                   _A	  =    """num_batches_tracked"""
                            else:
                                   _A	  =    None
                            set_recursively(snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__)
                     continue
              if not is_used:
                     unused_weights.append(snake_case__)

       logger.warning(F'''Unused weights: {unused_weights}''')






@torch.no_grad()
def 					snake_case    (			snake_case__     :Any  ,      snake_case__     :Union[str, Any]  ,      snake_case__     :str  ,      snake_case__     :Any=None  ,      snake_case__     :Any=None  ,      )		->  Union[str, Any]:
       if config_path is not None:
              _A	  =    EncodecConfig.from_pretrained(snake_case__)
       else:
              _A	  =    EncodecConfig()

       if model_name == "encodec_24khz":
              pass  # config is already correct
       elif model_name == "encodec_32khz":
              _A	  =    [8, 5, 4, 4]
              _A	  =    [2.2]
              _A	  =    64
              _A	  =    32_000
              _A	  =    2_048
              _A	  =    False
              _A	  =    False
              _A	  =    False
       elif model_name == "encodec_48khz":
              _A	  =    [8, 5, 4, 2]
              _A	  =    [3.0, 6.0, 12.0, 24.0]
              _A	  =    48_000
              _A	  =    2
              _A	  =    False
              _A	  =    """time_group_norm"""
              _A	  =    True
              _A	  =    1.0
              _A	  =    0.01
       else:
              raise ValueError(F'''Unknown model name: {model_name}''')

       _A	  =    EncodecModel(snake_case__)

       _A	  =    EncodecFeatureExtractor(
           feature_size=config.audio_channels  ,      sampling_rate=config.sampling_rate  ,      chunk_length_s=config.chunk_length_s  ,      overlap=config.overlap  ,      )
       feature_extractor.save_pretrained(snake_case__)

       _A	  =    torch.load(snake_case__)
       if "best_state" in original_checkpoint:
              # we might have a training state saved, in which case discard the yaml results and just retain the weights
              _A	  =    original_checkpoint["""best_state"""]
       recursively_load_weights(snake_case__  ,      snake_case__  ,      snake_case__)
       model.save_pretrained(snake_case__)

       if repo_id:
              print("""Pushing to the hub...""")
              feature_extractor.push_to_hub(snake_case__)
              model.push_to_hub(snake_case__)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE     				=		argparse.ArgumentParser()
    parser.add_argument(
        '--model',
        default='encodec_24khz',
        type=str,
        help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
    )
    parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
    )

    _SCREAMING_SNAKE_CASE     				=		parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase				:     Optional[int]					=				{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase				:     int					=				[
					    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SwinForImageClassification""",
					    """SwinForMaskedImageModeling""",
					    """SwinModel""",
					    """SwinPreTrainedModel""",
					    """SwinBackbone""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase				:     str					=				[
					    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFSwinForImageClassification""",
					    """TFSwinForMaskedImageModeling""",
					    """TFSwinModel""",
					    """TFSwinPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swin import (
										    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinBackbone,
										    SwinForImageClassification,
										    SwinForMaskedImageModeling,
										    SwinModel,
										    SwinPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_swin import (
										    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSwinForImageClassification,
										    TFSwinForMaskedImageModeling,
										    TFSwinModel,
										    TFSwinPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase				:     int					=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



